Signal-Transformer: A Robust and Interpretable Method for Rotating Machinery Intelligent Fault Diagnosis Under Variable Operating Conditions

As well-known, deep learning models have achieved great success in the field of intelligent fault diagnosis. However, once the working condition changed, the diagnostic accuracy of the trained models would be greatly affected, which seriously limits the application of deep learning models in real industry. Therefore, signal-transformer (S-Transformer), an intelligent fault diagnosis model focusing on the problem of variable operating conditions, is proposed in this study. First, signal embedding is employed to complete the segmentation and up-dimensional representation of the 1-D signal, thus enriching the information in a high dimensional space. Then, those embedded subsignals are further processed via the multihead self-attention mechanism to explore the state features of the signal in different spaces for a deep representation. Finally, an attention visualization method is proposed for vibrating signals to increase the interpretability of the proposed model and overcome the drawbacks of black boxes. With an experimental validation, the proposed model outperforms than other six deep learning methods in terms of diagnostic accuracy under unknown operating conditions. According to the principle of the model, the crucial weights for signal are further explained in visualization. Furthermore, it can be foreseen that the S-Transformer can achieve a robust recognition effect with the ability of interpretable feature extraction in signal denoising.